Respiration

This chapter discusses the respiratory system of giraffes. The respiratory system supplies oxygen, removes of carbon dioxide and produces the airflow needed to make sounds. Giraffes do not have the velocity of airflow through the airways to vibrate vocal cords sufficiently to generate sounds able to be heard by humans but can produce sounds able to be heard by giraffes. Air reaches alveoli for gas exchange through a long trachea, which is relatively narrow (~4 cm in diameter). Dead space volume is large. A short trunk and rigid chest wall reduce the capacity of the thorax and consequently lung volume is small. Respiratory rate is low (~10 min-1), but tidal volume is relatively big, and alveolar ventilation rate (VA; ~60 L min-1) delivers sufficient air despite the large dead space volume. Laryngeal muscles act to prevent food from entering the trachea a process controlled by the (short) superior and (long) inferior (recurrent) laryngeal nerves. Air that has been delivered to alveoli comes into contact with pulmonary artery blood (=cardiac output, Q; ~40 L min-1). The VA: Q ratio is ~1.5 (cf 0.8 in humans). Gas exchange occurs by diffusion. The surface area for diffusion is related to the number of alveoli which increase in number during growth from ~1 billion in a newborn giraffe to 11 billion in an adult. Gas carriage of oxygen and carbon dioxide is a function of erythrocytes which are small (MCV = 12 fL) but numerous (12 × 1012 L-1) and each liter of blood contains ~150 g of hemoglobin.

High-frequency ventilation

Physiological Reviews ◽

10.1152/physrev.1984.64.2.505 ◽

1984 ◽

Vol 64 (2) ◽

pp. 505-543 ◽

Cited By ~ 81

Author(s):

J. M. Drazen ◽

R. D. Kamm ◽

A. S. Slutsky

Keyword(s):

Experimental Data ◽

Gas Exchange ◽

Dead Space ◽

Gas Transport ◽

Physical Models ◽

High Frequency Ventilation ◽

General Description ◽

Wide Range ◽

Dead Space Volume ◽

Complete physiological understanding of HFV requires knowledge of four general classes of information: 1) the distribution of airflow within the lung over a wide range of frequencies and VT (sect. IVA), 2) an understanding of the basic mechanisms whereby the local airflows lead to gas transport (sect. IVB), 3) a computational or theoretical model in which transport mechanisms are cast in such a form that they can be used to predict overall gas transport rates (sect. IVC), and 4) an experimental data base (sect. VI) that can be compared to model predictions. When compared with available experimental data, it becomes clear that none of the proposed models adequately describes all the experimental findings. Although the model of Kamm et al. is the only one capable of simulating the transition from small to large VT (as compared to dead-space volume), it fails to predict the gas transport observed experimentally with VT less than equipment dead space. The Fredberg model is not capable of predicting the observed tendency for VT to be a more important determinant of gas exchange than is frequency. The remaining models predict a greater influence of VT than frequency on gas transport (consistent with experimental observations) but in their current form cannot simulate the additional gas exchange associated with VT in excess of the dead-space volume nor the decreased efficacy of HFV above certain critical frequencies observed in both animals and humans. Thus all of these models are probably inadequate in detail. One important aspect of these various models is that some are based on transport experiments done in appropriately scaled physical models, whereas others are entirely theoretical. The experimental models are probably most useful in the prediction of pulmonary gas transport rates, whereas the physical models are of greater value in identifying the specific transport mechanism(s) responsible for gas exchange. However, both classes require a knowledge of the factors governing the distribution of airflow under the circumstances of study as well as requiring detail about lung anatomy and airway physical properties. Only when such factors are fully understood and incorporated into a general description of gas exchange by HFV will it be possible to predict or explain all experimental or clinical findings.

Labeled carbon dioxide (C18O2): an indicator gas for phase II in expirograms

10.1152/japplphysiol.01360.2003 ◽

2004 ◽

Vol 97 (5) ◽

pp. 1755-1762 ◽

Cited By ~ 9

Author(s):

Holger Schulz ◽

Anne Schulz ◽

Gunter Eder ◽

Joachim Heyder

Keyword(s):

Carbon Dioxide ◽

Phase Ii ◽

Dead Space ◽

Moment Analysis ◽

Cumulative Distribution ◽

Breath Holding ◽

Power Moment ◽

Single Breath ◽

Dead Space Volume ◽

Carbon dioxide labeled with 18O (C18O2) was used as a tracer gas for single-breath measurements in six anesthetized, mechanically ventilated beagle dogs. C18O2 is taken up quasi-instantaneously in the gas-exchanging region of the lungs but much less so in the conducting airways. Its use allows a clear separation of phase II in an expirogram even from diseased individuals and excludes the influence of alveolar concentration differences. Phase II of a C18O2 expirogram mathematically corresponds to the cumulative distribution of bronchial pathways to be traversed completely in the course of exhalation. The derivative of this cumulative distribution with respect to respired volume was submitted to a power moment analysis to characterize volumetric mean (position), standard deviation (broadness), and skewness (asymmetry) of phase II. Position is an estimate of dead space volume, whereas broadness and skewness are measures of the range and asymmetry of functional airway pathway lengths. The effects of changing ventilatory patterns and of changes in airway size (via carbachol-induced bronchoconstriction) were studied. Increasing inspiratory or expiratory flow rates or tidal volume had only minor influence on position and shape of phase II. With the introduction of a postinspiratory breath hold, phase II was continually shifted toward the airway opening (maximum 45% at 16 s) and became steeper by up to 16%, whereas skewness showed a biphasic response with a moderate decrease at short breath holding and a significant increase at longer breath holds. Stepwise bronchoconstriction decreased position up to 45 ± 2% and broadness of phase II up to 43 ± 4%, whereas skewness was increased up to twofold at high-carbachol concentrations. Under all circumstances, position of phase II by power moment analysis and dead space volume by the Fowler technique agreed closely in our healthy dogs. Overall, power moment analysis provides a more comprehensive view on phase II of single-breath expirograms than conventional dead space volume determinations and may be useful for respiratory physiology studies as well as for the study of diseased lungs.

Computations of State Ventilation and Respiratory Parameters

10.1101/2021.11.08.21266078 ◽

2021 ◽

Author(s):

Quangang Yang

Keyword(s):

Dead Space ◽

Minute Ventilation ◽

Alveolar Ventilation ◽

Co2 Production ◽

Target Variable ◽

Respiratory Parameters ◽

Ventilation Support ◽

Dead Space Volume ◽

The Impact ◽

Background: In mechanical ventilation, there are still some challenges to turn a modern ventilator into a fully reactive device, such as lack of a comprehensive target variable and the unbridged gap between input parameters and output results. This paper aims to present a state ventilation which can provide a measure of two primary, but heterogenous, ventilation support goals. The paper also tries to develop a method to compute, rather than estimate, respiratory parameters to obtain the underlying causal information. Methods: This paper presents a state ventilation, which is calculated based on minute ventilation and blood gas partial pressures, to evaluate the efficacy of ventilation support and indicate disease progression. Through mathematical analysis, formulae are derived to compute dead space volume/ventilation, alveolar ventilation, and CO2 production. Results: Measurements from a reported clinical study are used to verify the analysis and demonstrate the application of derived formulae. The state ventilation gives the expected trend to show patient status, and the calculated mean values of dead space volume, alveolar ventilation, and CO2 production are 158mL, 8.8L/m, and 0.45L/m respectively for a group of patients. Discussions and Conclusions: State ventilation can be used as a target variable since it reflects patient respiratory effort and gas exchange. The derived formulas provide a means to accurately and continuously compute respiratory parameters using routinely available measurements to characterize the impact of different contributing factors.

Gas Exchange during Exercise in Healthy People: I. the Physiological Dead-Space Volume

Clinical Science ◽

10.1042/cs0510323 ◽

1976 ◽

Vol 51 (4) ◽

pp. 323-333 ◽

Cited By ~ 2

Author(s):

Christine A. Bradley ◽

E. A. Harris ◽

Eve R. Seelye ◽

R. M. L. Whitlock

Keyword(s):

Carbon Dioxide ◽

Dead Space ◽

Confidence Limit ◽

Carbon Dioxide Tension ◽

Physiological Dead Space ◽

Carbon Dioxide Output ◽

Pulmonary Capillary ◽

Dead Space Volume ◽

Capillary Temperature ◽

1. Physiological dead-space volume (VD) was measured in twenty-four healthy men and women aged from 20 to 71 years, at rest and at two rates of work on a treadmill, whilst breathing air and breathing oxygen. 2. The effect of correction of arterial carbon dioxide tension (Pa,co2) to pulmonary capillary temperature on the resulting value for VD was investigated. We find that the effect is substantial and that a correction should be made. 3. Equations have been derived for the prediction of normal VD during exercise. The best prediction was given by a regression on height, age, carbon dioxide output, ventilation and respiratory frequency, with an upper 95% confidence limit of +81 ml.

Reproduction of MIGET retention and excretion data using a simple mathematical model of gas exchange in lung damage caused by oleic acid infusion

10.1152/japplphysiol.01481.2005 ◽

2006 ◽

Vol 101 (3) ◽

pp. 826-832 ◽

Cited By ~ 29

Author(s):

S. E. Rees ◽

S. Kjærgaard ◽

S. Andreassen ◽

G. Hedenstierna

Keyword(s):

Oleic Acid ◽

Standard Deviation ◽

Simple Model ◽

Gas Exchange ◽

Dead Space ◽

Good Description ◽

Lung Damage ◽

Acid Infusion ◽

Dead Space Volume ◽

The multiple inert-gas elimination technique (MIGET) is a complex mathematical model and experimental technique for understanding pulmonary gas exchange. Simpler mathematical models have been proposed that have a limited view compared with MIGET but may be applicable for use in clinical practice. This study examined the use of a simple model of gas exchange to describe MIGET retention and excretion data in seven pigs before and following lung damage caused by oleic acid infusion and subsequently at different levels of positive end-expiratory pressure. The simple model was found to give, on average, a good description of MIGET data, as evaluated by a χ2 test on the weighted residual sum of squares resulting from the model fit ( P > 0.2). Values of the simple model's parameters (dead-space volume, shunt, and the fraction of alveolar ventilation going to compartment 2) compared well with the similar MIGET parameters (dead-space volume, shunt, log of the standard deviation of the perfusion, log of the standard deveation of the ventilation), giving values of bias and standard deviation on the differences between dead-space volume and shunt of 0.002 ± 0.002 liter and 7.3 ± 2.1% (% of shunt value), respectively. Values of the fraction of alveolar ventilation going to compartment 2 correlated well with log of the standard deviation of the perfusion ( r2 = 0.86) and log of the standard deviation of the ventilation ( r2 = 0.92). These results indicate that this simple model provides a good description of lung pathology following oleic acid infusion. It remains to be seen whether physiologically valid values of the simple model parameters can be obtained from clinical experiments varying inspired oxygen fraction. If so, this may indicate a role for simple models in the clinical interpretation of gas exchange.

Dead Space Volume in N95 Masks

Archivos de Bronconeumología ◽

10.1016/j.arbres.2020.11.006 ◽

2020 ◽

Author(s):

Santiago C. Arce ◽

Fernando Chiodetti ◽

Eduardo L. De Vito

Keyword(s):

Dead Space ◽

Dead Space Volume ◽

Gas exchange in healthy rabbits during high-frequency oscillatory ventilation

10.1152/jappl.1989.66.3.1343 ◽

1989 ◽

Vol 66 (3) ◽

pp. 1343-1351 ◽

Cited By ~ 40

Author(s):

B. R. Boynton ◽

M. D. Hammond ◽

J. J. Fredberg ◽

B. G. Buckley ◽

D. Villanueva ◽

...

Keyword(s):

Gas Exchange ◽

High Frequency ◽

High Frequency Oscillatory Ventilation ◽

Blood Gas ◽

Mean Airway Pressure ◽

Oscillatory Ventilation ◽

Dead Space Volume ◽

Alveolar Hypoventilation ◽

High Frequency Oscillatory ◽

We examined the effects of oscillatory frequency (f), tidal volume (VT), and mean airway pressure (Paw) on respiratory gas exchange during high-frequency oscillatory ventilation of healthy anesthetized rabbits. Frequencies from 3 to 30 Hz, VT from 0.4 to 2.0 ml/kg body wt (approximately 20–100% of dead space volume), and Paw from 5 to 20 cmH2O were studied. As expected, both arterial partial pressure of O2 and CO2 (PaO2 and PaCO2, respectively) were found to be related to f and VT. Changing Paw had little effect on blood gas tensions. Similar values of PaO2 and PaCO2 were obtained at many different combinations of f and VT. These relationships collapsed onto a single curve when blood gas tensions were plotted as functions of f multiplied by the square of VT (f. VT2). Simultaneous tracheal and alveolar gas samples showed that the gradient for PO2 and PCO2 increased as f. VT2 decreased, indicating alveolar hypoventilation. However, venous admixture also increased as f. VT2 decreased, suggesting that ventilation-perfusion inequality must also have increased.

Gas transport during high-frequency ventilation

10.1152/jappl.1983.55.2.472 ◽

1983 ◽

Vol 55 (2) ◽

pp. 472-478 ◽

Cited By ~ 15

Author(s):

V. Brusasco ◽

T. J. Knopp ◽

K. Rehder

Keyword(s):

Stroke Volume ◽

Dead Space ◽

High Frequency ◽

Oscillation Frequency ◽

High Frequency Ventilation ◽

Dead Space Volume ◽

Entire Lung ◽

Frequency Ventilation ◽

Gas Volume ◽

During high-frequency small-volume ventilation (HFV), the transport rate of gas from the mouth to a lung region is a function of two conductances (conductance is the transfer rate of a gas divided by its partial pressure difference): regional longitudinal gas conductance along the airways (Grlongi) and gas conductance between lung regions (Ginter). Grlongi per unit regional lung (gas) volume [Grlongi/(Vr beta g)] was determined during HFV in 11 anesthetized paralyzed dogs lying supine. The distribution of Grlongi/(Vr beta g) was nearly uniform during HFV when stroke volumes were less than approximately two-thirds of the Fowler dead-space volume. By contrast, the distribution of Grlongi/(Vr beta g) was nonuniform when the stroke volume exceeded approximately two-thirds of the Fowler dead-space volume and the oscillation frequency was 5 Hz. Gas conductance along the airways per unit lung gas volume [average Glongi/(V beta g)], for the entire lung, increased with stroke volume at all frequencies, but for a given product of oscillation frequency and stroke volume, the average Glongi/(V beta g) was greater when stroke volume was large and oscillation frequency was low. The average Glongi/(V beta g) increased with frequency up to a maximal value; the frequency at which the maximum occurred depended on the kinematic viscosity of the inspired gas mixture.

Reductions in dead space ventilation with nasal high flow depend on physiological dead space volume: metabolic hood measurements during sleep in patients with COPD and controls

European Respiratory Journal ◽

10.1183/13993003.02251-2017 ◽

2018 ◽

Vol 51 (5) ◽

pp. 1702251 ◽

Cited By ~ 19

Author(s):

Paolo Biselli ◽

Kathrin Fricke ◽

Ludger Grote ◽

Andrew T. Braun ◽

Jason Kirkness ◽

...

Keyword(s):

Respiratory Rate ◽

Dead Space ◽

Minute Ventilation ◽

High Flow ◽

Physiological Dead Space ◽

Copd Patients ◽

Dead Space Volume ◽

Anatomical Dead Space ◽

Dead Space Ventilation ◽

Nasal high flow (NHF) reduces minute ventilation and ventilatory loads during sleep but the mechanisms are not clear. We hypothesised NHF reduces ventilation in proportion to physiological but not anatomical dead space.11 subjects (five controls and six chronic obstructive pulmonary disease (COPD) patients) underwent polysomnography with transcutaneous carbon dioxide (CO2) monitoring under a metabolic hood. During stable non-rapid eye movement stage 2 sleep, subjects received NHF (20 L·min−1) intermittently for periods of 5–10 min. We measured CO2 production and calculated dead space ventilation.Controls and COPD patients responded similarly to NHF. NHF reduced minute ventilation (from 5.6±0.4 to 4.8±0.4 L·min−1; p<0.05) and tidal volume (from 0.34±0.03 to 0.3±0.03 L; p<0.05) without a change in energy expenditure, transcutaneous CO2 or alveolar ventilation. There was a significant decrease in dead space ventilation (from 2.5±0.4 to 1.6±0.4 L·min−1; p<0.05), but not in respiratory rate. The reduction in dead space ventilation correlated with baseline physiological dead space fraction (r2=0.36; p<0.05), but not with respiratory rate or anatomical dead space volume.During sleep, NHF decreases minute ventilation due to an overall reduction in dead space ventilation in proportion to the extent of baseline physiological dead space fraction.